10 citations · 20 across the 11 of their papers we have counts for
11 papers
Optimizing Large Language Models for Turkish: New Methodologies in Corpus Selection and Training
H. Toprak Kesgin, M. Kaan Yuce, Eren Dogan +7
In this study, we develop and assess new corpus selection and training methodologies to improve the effectiveness of Turkish language models. Specifically, we adapted Large Languag…
Cosmos-LLaVA: Chatting with the Visual Cosmos-LLaVA: Görselle Sohbet Etmek
Ahmed Zeer, Eren Dogan, Yusuf Erdem +7
In this study, a Turkish visual instruction model was developed and various model architectures and dataset combinations were analysed to improve the performance of this model. The…
Scaling BERT Models for Turkish Automatic Punctuation and Capitalization Correction
Abdulkader Saoud, Mahmut Alomeyr, Himmet Toprak Kesgin +1
This paper investigates the effectiveness of BERT based models for automated punctuation and capitalization corrections in Turkish texts across five distinct model sizes. The model…
Introducing cosmosGPT: Monolingual Training for Turkish Language Models
H. Toprak Kesgin, M. Kaan Yuce, Eren Dogan +5
The number of open source language models that can produce Turkish is increasing day by day, as in other languages. In order to create the basic versions of such models, the traini…
Türkçe Dil Modellerinin Performans Karşılaştırması Performance Comparison of Turkish Language Models
Eren Dogan, M. Egemen Uzun, Atahan Uz +6
The developments that language models have provided in fulfilling almost all kinds of tasks have attracted the attention of not only researchers but also the society and have enabl…
Advancing NLP Models with Strategic Text Augmentation: A Comprehensive Study of Augmentation Methods and Curriculum Strategies
Himmet Toprak Kesgin, Mehmet Fatih Amasyali
This study conducts a thorough evaluation of text augmentation techniques across a variety of datasets and natural language processing (NLP) tasks to address the lack of reliable,…
Investigating Semi-Supervised Learning Algorithms in Text Datasets
Himmet Toprak Kesgin, Mehmet Fatih Amasyali
Using large training datasets enhances the generalization capabilities of neural networks. Semi-supervised learning (SSL) is useful when there are few labeled data and a lot of unl…
Iterative Mask Filling: An Effective Text Augmentation Method Using Masked Language Modeling
Himmet Toprak Kesgin, Mehmet Fatih Amasyali
Data augmentation is an effective technique for improving the performance of machine learning models. However, it has not been explored as extensively in natural language processin…
Developing and Evaluating Tiny to Medium-Sized Turkish BERT Models
Himmet Toprak Kesgin, Muzaffer Kaan Yuce, Mehmet Fatih Amasyali
This study introduces and evaluates tiny, mini, small, and medium-sized uncased Turkish BERT models, aiming to bridge the research gap in less-resourced languages. We trained these…
A Robust Optimization Method for Label Noisy Datasets Based on Adaptive Threshold: Adaptive-k
Enes Dedeoglu, Himmet Toprak Kesgin, Mehmet Fatih Amasyali
SGD does not produce robust results on datasets with label noise. Because the gradients calculated according to the losses of the noisy samples cause the optimization process to go…
Cyclical Curriculum Learning
H. Toprak Kesgin, M. Fatih Amasyali
Artificial neural networks (ANN) are inspired by human learning. However, unlike human education, classical ANN does not use a curriculum. Curriculum Learning (CL) refers to the pr…